Determination of the distribution of strong coupling constant with machine learning
Abstract
In this work, we use the artificial neural network (ANN) method to study and predict the distribution of strong coupling constants by fitting the existing data. Our approach takes advantage of the ability of ANN to learn complex nonlinear relations and excellent generalization, and allows for a systematic treatment of the uncertainties associated with the data. To ensure the reliability of our results, we apply three evaluation indexes to evaluate the accuracy of model during training. Finally, we obtained the predicted values of the strong coupling constants at different energy scales, and compared and verified them with the existing experimental data. Our approach represents a promising way to improve the determination of the strong coupling constant at low energies, and could have important implications for future experimental and theoretical studies in quantum chromodynamics.
Keywords
Cite
@article{arxiv.2303.07968,
title = {Determination of the distribution of strong coupling constant with machine learning},
author = {Xiao-Yun Wang and Chen Dong and Quanjin Wang},
journal= {arXiv preprint arXiv:2303.07968},
year = {2023}
}
Comments
This work is a very preliminary result of using machine learning to study strong coupling constants. It is not a very mature work, and there are still certain uncertainties in the results. In a responsible manner, we decided to withdraw this manuscript after deliberation, and readers are invited to pay attention to our follow-up work on strong coupling constants in machine learning